Lange Rael T, Schoenberg Mike R, Saklofske Donald H, Woodward Todd S, Brickell Tracey A
Riverview Hospital, Coquitlam, British Columbia, Canada.
J Clin Exp Neuropsychol. 2006 Jul;28(5):773-89. doi: 10.1080/13803390591000981.
Since the release of the Canadian WAIS-III normative data in 2001 (Wechsler, 2001), the clinical application of these norms has been limited by the absence of a method to estimate premorbid functioning. However, Lange, Schoenberg, Woodward, and Brickell (2005) recently developed regression algorithms that estimate premorbid FSIQ, VIQ and PIQ scores for use with the Canadian WAIS-III norms. The purpose of this study was to expand work by Lange and colleagues by developing regression algorithms to estimate premorbid GAI (Saklofske et al., 2005), VCI, and POI scores. Participants were the Canadian WAIS-III standardization sample (n = 1,105). The sample was randomly divided into two groups (Development and Validation group). Using the Development group, a total of 14 regression algorithms were generated to estimate GAI, VCI, and POI scores by combining subtest performance (i.e., Vocabulary, Information, Matrix Reasoning, and Picture Completion) with demographic variables (i.e., age, education, ethnicity, region of the country, and gender). The algorithms accounted for a maximum of 77% of the variance in GAI, 78% of the variance in VCI, and 63% of the variance in POI. In the Validation Group, correlations between predicted and obtained scores were high (GAI = .70 to .88; VCI = .87 to .88; POI = .71 to .80). Evaluation of prediction errors revealed that the majority of estimated GAI, VCI, and POI scores fell within a 95% CI band (93.5% to 97.0%) and within 10 points of obtained index scores (72.3% to 85.6%) depending on the subtests used. These algorithms provide a promising means for estimating premorbid GAI, VCI, and POI scores using the Canadian WAIS-III norms.
自2001年加拿大韦氏成人智力量表第三版（WAIS-III）常模数据发布以来（韦克斯勒，2001年），由于缺乏估计病前功能的方法，这些常模的临床应用受到了限制。然而，兰格、舍恩伯格、伍德沃德和布里克尔（2005年）最近开发了回归算法，用于估计与加拿大WAIS-III常模一起使用的病前全量表智商（FSIQ）、言语智商（VIQ）和操作智商（PIQ）分数。本研究的目的是通过开发回归算法来估计病前综合智商（GAI）（萨克洛夫斯基等人，2005年）、言语理解指数（VCI）和知觉组织指数（POI）分数，从而扩展兰格及其同事的工作。参与者为加拿大WAIS-III标准化样本（n = 1105）。样本被随机分为两组（开发组和验证组）。利用开发组，通过将分测验成绩（即词汇、知识、矩阵推理和图片完成）与人口统计学变量（即年龄、教育程度、种族、国家地区和性别）相结合，总共生成了14种回归算法，以估计GAI、VCI和POI分数。这些算法解释了GAI中最多77%的方差、VCI中78%的方差以及POI中63%的方差。在验证组中，预测分数与实际获得分数之间的相关性很高（GAI = 0.70至0.88；VCI = 0.87至0.88；POI = 0.71至0.80）。对预测误差的评估表明，根据所使用的分测验，大多数估计的GAI、VCI和POI分数落在95%置信区间范围内（93.5%至97.0%），并且与实际获得的指数分数相差在10分以内（72.3%至85.6%）。这些算法为使用加拿大WAIS-III常模估计病前GAI、VCI和POI分数提供了一种很有前景的方法。
